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Record W2782735078 · doi:10.1115/imece2017-72092

A Novel Algorithm for Solving the Assembly Line Balancing Type I Problem

2017· article· en· W2782735078 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAssembly Line Balancing Optimization
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsComputer scienceWorkstationComponent (thermodynamics)Representation (politics)Assembly lineAlgorithmContext (archaeology)GraphSet (abstract data type)Theoretical computer scienceProgramming languageEngineering

Abstract

fetched live from OpenAlex

This paper presents a new modeling approach called Progressive Modeling (PM) and demonstrates it by solving the Assembly Line Balancing Type I Problem. PM introduces some new concepts that make the modeling process of large-scale complex industrial problems more systematic and their solution algorithms much faster and easily maintained. In the context of SALBP-I, PM introduces a component model to deploy the problem logic and its solution algorithm into several interacting components. The problem is represented as an object-oriented graph G (V, E, W) of vertices, edges, and workstations which enables problem solutions to start anywhere. The novel representation relaxes the only forward and backward tracking approach used in the assembly line balancing literature. A set of well-reported problems in the literature are reported and solved. The paper concludes by demonstrating the efficiency of the new modeling approach and future extensions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.428
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.255
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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